Nitrogen cycles in terrestrial ecosystems: climate change impacts and mitigation
Bibliographic record
Abstract
The nitrogen (N) cycle and N balance have primarily been modified by anthropogenic activities and environmental changes at various scales, including biological individual, ecosystem, local landscape, continental region, and global. These modifications have drastically affected the structures and functions of natural and agricultural ecosystems in terrestrial and aquatic areas. In this manuscript, we first present a modified view of the global N cycle that includes N transport, conversion, and exchange processes. Second, several crucial issues concerning N balance, including N deposition and excessive addition and the dynamics of N and other nutrients, are reviewed. Third, the effects of climate change factors, including water status, warming, and elevated CO2 concentrations, on N balance and the N cycle and their interactions within and with other environmental factors are outlined. Finally, intervention strategies for improving N balance and N cycling to address rapid continual climatic change and socio-economic development are presented and discussed. It is highlighted that the altered N balance and N cycle between the geosphere, biosphere, and atmosphere have produced the profoundly critical challenge of maintaining N levels within an appropriate range, which should be considered by relevant people and sectors, including researchers, managers, and policy makers from ecological, environmental, and sustainable development sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".